Large scale model of the olfactory bulb (Yu et al., 2013)

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Accession:144570
The readme file currently contains links to the results for all the 72 odors investigated in the paper, and the movie showing the network activity during learning of odor k3-3 (an aliphatic ketone).
Reference:
1 . Yu Y, McTavish TS, Hines ML, Shepherd GM, Valenti C, Migliore M (2013) Sparse distributed representation of odors in a large-scale olfactory bulb circuit. PLoS Comput Biol 9:e1003014 [PubMed]
Model Information (Click on a link to find other models with that property)
Model Type: Realistic Network; Channel/Receptor; Dendrite;
Brain Region(s)/Organism: Olfactory bulb;
Cell Type(s): Olfactory bulb main mitral GLU cell; Olfactory bulb main interneuron granule MC GABA cell;
Channel(s): I Na,t; I A; I K;
Gap Junctions:
Receptor(s): NMDA; Glutamate; Gaba;
Gene(s):
Transmitter(s): Gaba; Glutamate;
Simulation Environment: NEURON;
Model Concept(s): Pattern Recognition; Activity Patterns; Bursting; Temporal Pattern Generation; Oscillations; Synchronization; Active Dendrites; Detailed Neuronal Models; Synaptic Plasticity; Action Potentials; Synaptic Integration; Unsupervised Learning; Olfaction;
Implementer(s): Hines, Michael [Michael.Hines at Yale.edu]; Migliore, Michele [Michele.Migliore at Yale.edu];
Search NeuronDB for information about:  Olfactory bulb main mitral GLU cell; Olfactory bulb main interneuron granule MC GABA cell; NMDA; Glutamate; Gaba; I Na,t; I A; I K; Gaba; Glutamate;
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YuEtAl2012
readme.html
ampanmda.mod
fi.mod
kamt.mod *
kdrmt.mod *
naxn.mod *
ThreshDetect.mod *
.hg_archival.txt
allsynhinton.hoc *
antest.ses *
clear.hoc *
connect.hoc
control.ses
default.hoc
granule.hoc *
hinton.hoc
init.hoc *
iterator.hoc *
lindgren.job
lptiter.hoc
mgrs.hoc
michele_movie.hoc
mitral.hoc
mosinit.hoc *
net.hoc
odors.txt
odors-forsim500-kensaku.txt
param.hoc
parinit.hoc
pattern.hoc
perfrun.hoc
record.hoc
show.hoc
showstim.hoc
showw.hoc
somesyn.hoc *
spike2file.hoc
spkdat2bin.hoc
split.hoc
start.hoc
start.ses *
stim-AB-rnd-500mt.hoc
stim-o11o12.hoc
stim-o14.hoc
stim-o26.hoc
stim-o26d1-mnoise5hz-gnoise-5s.hoc
stim-o5high-o6low.hoc
stim-odors-AB-seq.hoc
stim-pair.hoc
stim-seq-rnd.hoc
subset.hoc
subset_control.ses *
viewspikes.hoc
viewspikes1.hoc
weight_movie.hoc *
weightsave.hoc
                            
{load_file("nrngui.hoc")}
{load_file("mitral.hoc")}
{load_file("granule.hoc")}

celsius = 35
objref nil, pc
pc = new ParallelContext()

// line of nmitral mitral cells distributed from position 0 to len.
// No wrap-around. Each mitral cell assumed to have two
// secondary dendrites along the line with length len.

//network size parameters
//change following in param.hoc 
//len = 1000 // size of the linear domain, mitral positions range from 0 to len
//Lsec = 1000 // mitral secden length
//ngranule = 1000 // distributed uniformly over the linear domain
//nmitral = 100 // distributed uniformly over the linear domain
// positions of mitral and granule cells
objref mitral_x, granule_x
mitral_x = new Vector()
granule_x = new Vector()
for i=0, nmitral - 1 {
	mitral_x.append(len/nmitral/2+i*len/nmitral)
}
for i=0, ngranule - 1 {
	granule_x.append(len/ngranule/2+i*len/ngranule)
}

nmitral=mitral_x.size

if (pc.id == 0) {
print nmitral, "first and last mitral at ", mitral_x.x[0], mitral_x.x[nmitral-1]
print ngranule, "first and last granule at ", granule_x.x[0], granule_x.x[ngranule-1]
}

// networksize assigned variables
// see comment on global GIDs assigned to cells

nmitral_begin = 0
ngranule_begin = nmitral_begin + nmitral
ncell = ngranule_begin + ngranule

{load_file("mgrs.hoc")}
{load_file("connect.hoc")}

// for more accurate complexity when splitting
// (prior to creation)
// $1 gid, $2 right or left
func how_many_syn_on_secden(){local n, xm, i, xg, delta, g  localobj sp
	if (object_id(sparse_connection_matrix_) == 0) {
		sparse_connections()
	}
	sp = sparse_connection_matrix_
	xm = mitral_x.x[$1]
	n = 0
	for i = 0, sp.sprowlen($1) - 1 {
		sp.spgetrowval($1, i, &g)
		xg = granule_x.x[i]
		delta = xg - xm
		if ($2 == 0) { // right secden
			if (delta > 0 && delta < Lsec) {
				n += 1
			}
		}else{ // left secden
			if (delta < 0 && delta > -Lsec) {
				n += 1
			}
		}
	}
	return n
}
func how_many_syn_on_granule() {local i, n, xg, xm  localobj c //$1 is gid
	c = g2m_connections($1)
	return c.size

	xg = granule_x.x[$1]
	n = 0
	for i=0, nmitral-1 {
		xm = mitral_x.x[i]
		if (abs(xm - xg) < Lsec) {
			n += 1
		}
	}
	return n
}
// if iterator does not exist load the default one.
if (!name_declared("cell_gids")) { load_file("iterator.hoc") }

//variables
// each cpu will have its own cell objects

objref cells  //mitrals and granules on this cpu
cells = new List()

// for initialization
Vrest=-65
proc init() {
	finitialize(Vrest)
	forall {
		if (ismembrane("nax")) {
			e_pas=v+(ina+ik)/g_pas
		} else if (ismembrane("k_ion")){
			e_pas=v+ik/g_pas
		}
	}
	fcurrent()
	cvode.re_init()
	frecord_init()
}

//parallel network object

// distribute GIDs and create cells

proc create_cells() { local gid, pgid, i, j, nm   localobj cell, nc
    for cell_gids(&pgid, &i) {   //iterator returns changed value of address &gid in each loop
	pc.set_gid2node(pgid, pc.id) //here the return value of iterator (gid) is used
    }
    for cell_gids(&pgid, &i) { 
	gid = basegid(pgid)
	// if a piece exists do not create again
	if (piecegid(gid) != pgid) { continue }
	if (gid < nmitral) {
	    cell = new Mitral()
	    cell.position(mitral_x.x[gid], gid*20, 0)
	    for j=0,1 cell.secden[j].L = Lsec
	    splitmitral(gid, cell) // also associates base and piece gids
	} else {
	    cell = new Granule()
	    cell.position(granule_x.x[gid-ngranule_begin], -100, 0)
	    cell.soma nc = new NetCon(&v(.5),nil)
	    pc.cell(gid, nc)
	}
	cells.append(cell)
//	print pc.id, gid, cell
 
    }
    if (is_split) { pc.multisplit() }
}

proc mknet() {
	create_cells()
	connect_cells()
}

//load_file("control_net_graphics.hoc")
//load_file("control_net3.hoc")
//for i=0, mgrs_list.count-1 mgrs_list.object(i).pr()

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